This invention relates generally to autonomous waste and/or recycling collection by a waste services provider during performance of a waste service activity.
It is known in the art to utilize a waste service vehicle to provide services to customers, which can include typical lines of waste industry services such as waste collection and transport and/or recycling for commercial, residential and/or industrial. However, certain existing systems and methods for servicing customers are inefficient, expensive and result in unnecessary delays.
Improvements to this technology are therefore desired.
The following presents a simplified summary of the disclosed subject matter in order to provide a basic understanding of some aspects thereof. This summary is not an exhaustive overview of the technology disclosed herein.
In certain illustrative embodiments, a system for improved waste collection in a residential neighborhood is provided. The system can include: a collection station at a centralized location in the neighborhood and configured to receive waste materials; a plurality of autonomous collection vehicles each capable of traveling on a respective travel path between the collection station and one or more residences in the residential neighborhood and configured to collect the waste materials from the residences and deliver the waste materials to the collection station; and a collection vehicle configured to collect the waste materials from the collection station and transport the waste materials away from the neighborhood to a collection site.
In certain aspects, the collection site can be a landfill. The collection station can include a fullness monitor and can be further configured to sort waste materials. The plurality of autonomous collection vehicles can be configured for static routing along the respective travel paths. The plurality of autonomous collection vehicles can be configured for dynamic routing along the respective travel paths. The plurality of autonomous collection vehicles can be configured for either static routing or dynamic routing along the respective travel paths. The configuration of the dynamic routing can be based upon customer collection patterns, customer holiday usage of collection services, load balancing for the autonomous collection vehicle, load balancing for the waste service vehicle, and/or determination of the charge remaining for the autonomous collection vehicles. The plurality of autonomous collection vehicles can be configured to collect waste containers from the residences and deliver the waste containers to the collection station.
In certain aspects, the system can include a communications link between the collection station and the collection vehicle and can be configured such that when the fullness monitor determines that the collection station is filled to a pre-determined level, an instruction is sent to the waste collection vehicle via the communications link to travel to the residential neighborhood to perform waste collections.
A better understanding of the presently disclosed subject matter can be obtained when the following detailed description is considered in conjunction with the following drawings, wherein:
While certain preferred illustrative embodiments will be described herein, it will be understood that this description is not intended to limit the subject matter to those embodiments. On the contrary, it is intended to cover all alternatives, modifications, and equivalents, as may be included within the spirit and scope of the subject matter as defined by the appended claims.
The presently disclosed subject matter relates to systems and methods for autonomous waste and/or recycling collection by a waste services provider during performance of a waste service activity. The presently disclosed systems and methods are directed to overcoming the issues and problems of the prior art.
In the particular illustrative embodiment of
In certain illustrative embodiments, the system 5 can include, without limitation: (i) a collection station 20 or “hub” at a centralized location in a residential neighborhood or group of neighborhoods; (ii) a plurality of autonomous collection vehicles 30 capable of traveling between the collection station 20 and the residences in the neighborhood to collect waste/items, recyclables and/or containers at the residences and deliver the waste/items, recyclables and/or containers to the collection station 20; and (iii) a waste service vehicle 10 capable of collecting waste/items from the collection station 20 and transporting the waste/items away from the neighborhood to a landfill or other collection, disposal or processing site.
As used throughout this document, the terms “residential” or “neighborhood” or the phrase “residential neighborhood,” or any other like terms or phrases, are used for explanatory purposes only and should not be seem as limiting, as the presently disclosed subject matter may be utilized in a residential, urban, industrial, commercial, mixed-use, apartments or condo, single-family or any other type of setting where waste or recycling services may be offered to customers.
In certain illustrative embodiments, the system 5 can include a collection station 20 or “hub” for storing waste and/or recyclables that have been collected from the neighborhood. The collection station 20 or “hub” can have a variety of different forms. For example, as shown in
In certain illustrative embodiments, the collection station 20 or “hub” can include equipment and related software for processing and identifying collected material to provide sustainability related performance to customers.
In certain illustrative embodiments, the system 5 can also include a plurality of autonomous collection vehicles 30. The autonomous collection vehicles 30 are configured for travelling between the collection station 20 and the residences in the neighborhood to collect waste/items and/or waste/item containers at the residences and deliver the waste/items and/or waste/item containers to the collection station 20. The collection station 20 can function as a home base, or “hub,” for the autonomous collection vehicles 30. The containers will typically be assigned to, or associated with, specific customers at specific residential addresses registered to a waste collection company.
The autonomous collection vehicles 30 can have a variety of different forms. For example, as shown in
In certain illustrative embodiments, the autonomous collection vehicles 30 can be electrically powered vehicles with zero/ultra-low emissions, and capable of receiving a charge to power up. As shown in
In certain illustrative embodiments, a set of programmed data can be associated with the routing for the autonomous collection vehicles 30. The set of programmed data can include, for example, customer-related data pertaining to a plurality of customer accounts. Such information may include customer location, route data, container weight, items expected to be removed from the customer site, billing data, and/or location information (e.g., street address, city, state, and zip code) of a customer site.
In certain illustrative embodiments, the programmed data can be utilized to develop “static” or “dynamic” routing for the autonomous collection vehicles 30.
In the context of “static” routing, the autonomous collection vehicles 30 can travel along a fixed, predefined path and have a set collection schedule and practice (e.g., pick-up of container waste from the same residence at the same address every Tuesday at 11 am).
In the context of “dynamic” routing, software algorithms and machine learning can be utilized to change or alter the path, schedule and/or practice of the autonomous collection vehicles 30 according to various factors. For example, machine learning can be utilized to “intelligently” alter the collection path, schedule and/or practice based on one or more factors associated with, e.g., customer activity such as, without limitation, collection patterns, holiday usage, different customer schedules (e.g., on call, on vacation, etc. . . . ), frequent users, overloaded containers, weight and/or load balancing for autonomous collection vehicles 30 and/or the waste service vehicle 10, charge remaining for autonomous collection vehicles 30, etc.
By way of example, as shown in
In certain illustrative embodiments, the system 5 can also include a waste service vehicle 10 capable of collecting waste/items from the collection station 20 and transporting the waste/items away from the neighborhood and to a landfill or other collection area that is off-site and remote from the neighborhood. Some examples of types of waste service vehicle 10 can include, for example, trucks such as front loader and rear loaders for commercial lines of business, automated side loaders and rear loaders for residential lines of business, and/or vehicles typically utilized for industrial lines of business. Typically, automated side loaders in residential lines of business perform service only on one side of the street (nearest the right side of the vehicle), while rear loaders can perform service on both sides of the street (right and left). All these vehicles 30 can also be equipped with onboard computer units (OBU) 90 that enable driver interactions with the OBU 90 and capturing of GPS location data and events corresponding to a service.
In certain illustrative embodiments, a communications network 100 may be adapted for use in the specific waste services environment of
The communications network 100 can include a plurality of data sources and a central server. Data sources may be, for example, devices configured for capturing and communicating operational data indicative of one or more operational characteristics of the system components (e.g., optical sensors such as cameras, scanners, RFID tags, bar codes, user input received via a user interface provided on a mobile phone, local or remote computer, etc. . . . ). Data sources are configured to communicate with a central server by sending and receiving operational data over a network (e.g., the Internet, an Intranet, or other suitable network).
In certain illustrative embodiments, system 5 is configured to pick up waste autonomously in sequence, and not on demand or by capacity. Moreover, in certain illustrative embodiments, the system 5 is configured to capture real time data for the autonomous collection vehicles 30 and the communication system and network 100 will help the user to route-optimize based on GPS coordinates and real time data.
In certain illustrative embodiments, the central server can be a local central server configured to process and evaluate the operational data received from data sources. Communications devices can be disposed on the system components. The communications devices and local central server are configured to communicate with each other via a communications network (e.g., the Internet, an Intranet, a cellular network, or other suitable network). In addition, communications device and central server are configured for storing data to an accessible central server database located on, or remotely from, the local central server. In the description provided herein, the system 5 may be configured for managing and evaluating the operation of a large fleet of waste service vehicles 10 and autonomous collections vehicles 30. As such, in certain illustrative embodiments, the system 5 may further comprise a plurality of communications devices 120, each being associated with one of a plurality of waste service vehicles 10 or autonomous collections vehicles 30.
In certain illustrative embodiments, the communication between the communications devices 120 and the local and/or remote central server may be provided on a real time basis such that during the collection route, data is transmitted from each waste service vehicle 10 or autonomous collection vehicle 30 to the desired central server. Alternatively, communication device 120 may be configured to temporarily store or cache data during the collection route and transfer the data to the desired central server on, e.g., return of the waste service vehicle 10 to the location of the waste collection company or when the autonomous collection vehicle 30 docks with the collection station 20 or “hub.”
In certain illustrative embodiments, waste service vehicle 10 can also include an onboard computer 130 and a location device 140. Onboard computer 130 can be, for example, a standard desktop or laptop personal computer (“PC”), or a computing apparatus that is physically integrated with vehicle 30, and can include and/or utilize various standard interfaces that can be used to communicate with the location device 140. Onboard computer 130 can also communicate with central server via a communications network via the communication device 120. The location device 140 can be configured to determine the location of the waste service vehicle 10 always while the waste service vehicle 10 is inactive, in motion and operating and performing service related and nonservice related activities. For example, location device 140 can be a GPS device that can communicate with the waste collection company. A satellite or other communications device 150 can be utilized to facilitate communications. For example, location device 140 can transmit location information, such as digital latitude and longitude, to onboard computer 130 via satellite. Thus, location device can identify the location of the waste service vehicle 10 at all times it is in operation.
Similarly, the autonomous collection vehicle 30 can include an onboard computer 150 and a location device 160. Onboard computer 150 can be, for example, a standard desktop or laptop personal computer (“PC”), or a computing apparatus that is physically integrated with vehicle, and can include and/or utilize various standard interfaces that can be used to communicate with the location device 160. Onboard computer 150 can also communicate with central server via a communications network via the communication device. The location device 160 can be configured to determine the location of the autonomous collection vehicle 30 always while the autonomous collection vehicle 30 is inactive, in motion and operating and performing service related and nonservice related activities. For example, location device 160 can be a GPS device that can communicate with the waste collection company. A satellite or other communications device can be utilized to facilitate communications. For example, location device 160 can transmit location information, such as digital latitude and longitude, to onboard computer 150 via satellite. Thus, location device 160 can identify the location of the autonomous collection vehicles 30 at all times.
In certain illustrative embodiments, central server can be configured to receive and store operational data (e.g., data received from waste services vehicle 10 or the autonomous collection vehicle 30) and evaluate the data to aid waste services company in improving operational efficiency. Central server can include various means for performing one or more functions in accordance with embodiments of the present invention, including those more particularly shown and described herein; however, central server may include alternative devices for performing one or more like functions without departing from the spirit and scope of the present invention.
In certain illustrative embodiments, central server can include standard components such as processor and user interface for inputting and displaying data, such as a keyboard and mouse or a touch screen, associated with a standard laptop or desktop computer. Central server also includes a communication device for wireless communication with onboard computer.
Central server may include software that communicates with one or more memory storage areas. Memory storage areas can be, for example, multiple data repositories which stores pre-recorded data pertaining to a plurality of customer accounts. Such information may include programmed data such as customer-related data pertaining to customer location, route data, items expected to be removed from the customer site, billing data, and/or location information (e.g., street address, city, state, and zip code) of a customer site. Using the location information for a customer site, software may find the corresponding customer account in memory storage areas. Database for data storage can be in memory storage area and/or supplementary external storage devices as are well known in the art.
While a “central server” is described herein, a person of ordinary skill in the art will recognize that embodiments of the present invention are not limited to a client-server architecture and that the server need not be centralized or limited to a single server, or similar network entity or mainframe computer system. Rather, the server and computing system described herein may refer to any combination of devices or entities adapted to perform the computing and networking functions, operations, and/or processes described herein without departing from the spirit and scope of embodiments of the present invention.
In certain illustrative embodiments, the presently disclosed systems and methods for autonomous waste and/or recycling collection by a waste services provider during performance of a waste service activity can utilize functionality and improvements to proprietary systems such as described in U.S. Pat. No. 10,594,991 issued Mar. 17, 2020, and assigned to WM Intellectual Property Holdings LLC and titled “System and method for managing service and non-service related activities associated with a waste collection, disposal and/or recycling vehicle,” the disclosure and contents of which are incorporated by reference herein in their entirety.
In certain illustrative embodiments, a system for improved waste collection or collection of items in a residential neighborhood is provided. The system can include: a collection station 20 or “hub” at a centralized location in the neighborhood, the collection station 20 having a fullness or capacity monitor and materials sorting capability; a plurality of autonomous collection vehicles 30 each capable of traveling on a respective travel path between the collection station 20 and the residences in the neighborhood to collect waste/items and/or waste/item containers at the residences and deliver the waste/items and/or waste/item containers to the collection station 20, wherein the plurality of autonomous collection vehicles 30 are configured for static and/or dynamic routing along the respective travel paths; a (waste) collection vehicle 10 capable of collecting waste/items from the collection station 20 and transporting the waste/items away from the neighborhood to a landfill or other collection site; and a communications link between the collection station 20 and the (waste) collection vehicle 10, such that when the fullness or capacity monitor indicates that the collection station 20 is filled to a certain level, the waste collection vehicle 10 will travel to the neighborhood to perform waste collections. In certain aspects: the neighborhood can include multiple adjacent/connected neighborhoods put together that will have autonomous collection vehicles 30 and collection stations 20; the autonomous collection vehicles 30 can include electric autonomous vehicles; the waste can include municipal solid waste (MSW), recyclables, organics, yard waste, and other deposited items; the collection site can include the “hub,” wherein the autonomous vehicle 30 can deliver the waste to the hub 20, and another vehicle which could be autonomous would then pick up the waste from the hub 20 to deliver it to a transfer station or landfill; the waste collection can be performed based on frequency such as once a week or twice a week regardless of fullness capacity; and/or the communication link can also be at the hub 20, and can notify a user that the hub 20 is full and a transportation vehicle can come and pick up the hub 20 and then bring that to a landfill or transfer station.
In certain illustrative embodiments, a customer facing portal 180 can be provided that allows customers to check items such as service status, sustainability data, billing etc. in connection with the presently disclosed system and method.
In certain illustrative embodiments, one or more action proposals can be generated based on information gathered by the waste service vehicle 10 and/or autonomous collection vehicle 30 during the waste service activity. The actions proposals can include, for example, recommendations to (i) remove excess waste from customer container, (ii) remove and replace container, (iii) provide additional containers, (iv) provide reporting, education and/or instructions to customer, or (v) to adjust customer billing.
In addition, historical account information and attributes of target customer and “like” customers can be collected, and the action proposals for target customers can be determined and ranked based on lifetime value impact scoring. Additional information can also be collected from the Internet or other outside sources. Scoring of target customer can be impacted based on prior proposals or interactions as well as preferences/acceptances of “like” customers to similar action proposals, and restrictions or constraints from target customer's attributes can be applied. Action proposals can be delivered to appropriate user/system for acceptance, and thereupon, the action proposal can be executed/applied, which can include charging the customer for the overage, notifying the customer of the overage through a proactive warning and notification process (including still images and/or video), and noting the overage incident on the customer's account.
In certain illustrative embodiments, a method is provided for collecting, processing, and applying data from a waste service vehicle 10 and/or autonomous collection vehicle 30 to increase customer lifetime value through targeted action proposals. The method can include the steps of: collecting information (such as image, video, collection vehicle, driver inputs) at a target service location; matching customer account to a target service location; processing information from the target service location and historical customer account record to create an action proposal; and executing an action from the action proposal. The information that can be processed can include a variety of gathered information from the waste service vehicle 10 and/or autonomous collection vehicle 30 and/or from other sources relating to the residential neighborhood and collections performed therein or therefor using the autonomous collection vehicles 30, for example, information regarding safety, receptacle condition, receptacle contents, fill status and/or contamination status, site conditions, obstructions (temporary or permanent), service, service quality (verification, receptacle identification, receptacle contents), service audit (size, frequency, location, and quantity), service exceptions (unable to service, site obstructions), site damage, theft/poaching/no customer, sustainability, material diversion/audits, dangerous/hazardous materials, savings, site service times, bin locations and ancillary services (locks, gates, etc).
In certain illustrative embodiments, machine learning workflows can also be utilized to augment the information gathered by the waste service vehicle 10 and/or autonomous collection vehicles 30. In certain illustrative embodiments, machine learning workflows can process commercial and/or residential overage and contamination events. An object detection model can support the overage and contamination workflows with contamination using an additional classification layer. Object detection can be utilized to identify objects of certain classes in an image, interpreting these images and make predictions. Capture of potentially millions of images and videos is possible using optical sensors, however, relevant metadata to help facilitate the creation of training datasets for machine learning can be limited. In certain illustrative embodiments, data can be curated and labeled for these specific purposes.
The presently disclosed subject matter has a variety of practical applications, as well as provides solutions to a number of technological and business problems of the prior art. For example, the presently disclosed waste management system can allow a waste service provider to more efficiently access and service customer waste containers during performance of a waste service activity. The presently disclosed waste management system can also provide improved sorting and material recovery, disposal load maximization, dynamic service schedules and other improved collection activities.
In certain illustrative embodiments, the system and method disclosed herein can also be utilized to perform collection or related services in industries other than the waste/recycling industry where automation, intelligent routing and associated computer functionality are utilized, such as, for example, package delivery, logistics, transportation, food delivery, ride hailing, couriers, freight transportation, etc.
Those skilled in the art will appreciate that certain portions of the subject matter disclosed herein may be embodied as a method, data processing system, or computer program product. Accordingly, these portions of the subject matter disclosed herein may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, portions of the subject matter disclosed herein may be a computer program product on a computer-usable storage medium having computer readable program code on the medium. Any suitable computer readable medium may be utilized including hard disks, CD-ROMs, optical storage devices, or other storage devices. Cloud storage can also be used. Further, the subject matter described herein may be embodied as systems, methods, devices, or components. Accordingly, embodiments may, for example, take the form of hardware, software or any combination thereof, and/or may exist as part of an overall system architecture within which the software will exist. The present detailed description is, therefore, not intended to be taken in a limiting sense.
As used herein, the phrase “at least one of” preceding a series of items, with the terms “and” or “or” to separate any of the items, modifies the list as a whole, rather than each member of the list (i.e., each item). The phrase “at least one of” allows a meaning that includes at least one of any one of the items, and/or at least one of any combination of the items, and/or at least one of each of the items. By way of example, the phrases “at least one of A, B, and C” or “at least one of A, B, or C” each refer to only A, only B, or only C; any combination of A, B, and C; and/or at least one of each of A, B, and C. As used herein, the term “A and/or B” means embodiments having element A alone, element B alone, or elements A and B taken together.
While the disclosed subject matter has been described in detail in connection with a number of embodiments, it is not limited to such disclosed embodiments. Rather, the disclosed subject matter can be modified to incorporate any number of variations, alterations, substitutions or equivalent arrangements not heretofore described, but which are commensurate with the scope of the disclosed subject matter.
Additionally, while various embodiments of the disclosed subject matter have been described, it is to be understood that aspects of the disclosed subject matter may include only some of the described embodiments. Accordingly, the disclosed subject matter is not to be seen as limited by the foregoing description, but is only limited by the scope of the claims.
It is to be understood that the present invention is not limited to the embodiment(s) described above and illustrated herein, but encompasses any and all variations falling within the scope of the appended claims.
This application claims the benefit, and priority benefit, of U.S. Provisional Patent Application Ser. No. 63/326,708, filed Apr. 1, 2022, the disclosure and contents of which are incorporated by reference herein in their entirety.
Number | Name | Date | Kind |
---|---|---|---|
3202305 | Hierpich | Aug 1965 | A |
5072833 | Hansen et al. | Dec 1991 | A |
5230393 | Mezey | Jul 1993 | A |
5245137 | Bowman et al. | Sep 1993 | A |
5278914 | Kinoshita et al. | Jan 1994 | A |
5489898 | Shigekusa et al. | Feb 1996 | A |
5762461 | Frohlingsdorf | Jun 1998 | A |
5837945 | Cornwell et al. | Nov 1998 | A |
6097995 | Tipton et al. | Aug 2000 | A |
6408261 | Durbin | Jun 2002 | B1 |
6448898 | Kasik | Sep 2002 | B1 |
6510376 | Burnstein et al. | Jan 2003 | B2 |
6563433 | Fujiwara | May 2003 | B2 |
6729540 | Ogawa | May 2004 | B2 |
6811030 | Compton et al. | Nov 2004 | B1 |
7146294 | Waitkus, Jr. | Dec 2006 | B1 |
7330128 | Lombardo et al. | Feb 2008 | B1 |
7383195 | Mallett et al. | Jun 2008 | B2 |
7406402 | Waitkus, Jr. | Jul 2008 | B1 |
7501951 | Maruca et al. | Mar 2009 | B2 |
7511611 | Sabino et al. | Mar 2009 | B2 |
7536457 | Miller | May 2009 | B2 |
7659827 | Gunderson et al. | Feb 2010 | B2 |
7804426 | Etcheson | Sep 2010 | B2 |
7817021 | Date et al. | Oct 2010 | B2 |
7870042 | Maruca et al. | Jan 2011 | B2 |
7878392 | Mayers et al. | Feb 2011 | B2 |
7957937 | Waitkus, Jr. | Jun 2011 | B2 |
7994909 | Maruca et al. | Aug 2011 | B2 |
7999688 | Healey et al. | Aug 2011 | B2 |
8020767 | Reeves et al. | Sep 2011 | B2 |
8056817 | Flood | Nov 2011 | B2 |
8146798 | Flood et al. | Apr 2012 | B2 |
8185277 | Flood et al. | May 2012 | B2 |
8269617 | Cook et al. | Sep 2012 | B2 |
8314708 | Gunderson et al. | Nov 2012 | B2 |
8330059 | Curotto | Dec 2012 | B2 |
8332247 | Bailey et al. | Dec 2012 | B1 |
8373567 | Denson | Feb 2013 | B2 |
8374746 | Plante | Feb 2013 | B2 |
8384540 | Reyes et al. | Feb 2013 | B2 |
8417632 | Robohm et al. | Apr 2013 | B2 |
8433617 | Goad et al. | Apr 2013 | B2 |
8485301 | Grubaugh et al. | Jul 2013 | B2 |
8508353 | Cook et al. | Aug 2013 | B2 |
8542121 | Maruca et al. | Sep 2013 | B2 |
8550252 | Borowski et al. | Oct 2013 | B2 |
8564426 | Cook et al. | Oct 2013 | B2 |
8564446 | Gunderson et al. | Oct 2013 | B2 |
8602298 | Gonen | Dec 2013 | B2 |
8606492 | Botnen | Dec 2013 | B1 |
8630773 | Lee et al. | Jan 2014 | B2 |
8645189 | Lyle | Feb 2014 | B2 |
8674243 | Curotto | Mar 2014 | B2 |
8676428 | Richardson et al. | Mar 2014 | B2 |
8714440 | Flood et al. | May 2014 | B2 |
8738423 | Lyle | May 2014 | B2 |
8744642 | Nemat-Nasser et al. | Jun 2014 | B2 |
8803695 | Denson | Aug 2014 | B2 |
8818908 | Altice et al. | Aug 2014 | B2 |
8849501 | Cook et al. | Sep 2014 | B2 |
8854199 | Cook et al. | Oct 2014 | B2 |
8862495 | Ritter | Oct 2014 | B2 |
8880279 | Plante | Nov 2014 | B2 |
8930072 | Lambert et al. | Jan 2015 | B1 |
8952819 | Nemat-Nasser | Feb 2015 | B2 |
8970703 | Thomas, II et al. | Mar 2015 | B1 |
8996234 | Tamari et al. | Mar 2015 | B1 |
9047721 | Botnen | Jun 2015 | B1 |
9058706 | Cheng | Jun 2015 | B2 |
9098884 | Borowski et al. | Aug 2015 | B2 |
9098956 | Lambert et al. | Aug 2015 | B2 |
9111453 | Alselimi | Aug 2015 | B1 |
9158962 | Nemat-Nasser et al. | Oct 2015 | B1 |
9180887 | Nemat-Nasser et al. | Nov 2015 | B2 |
9189899 | Cook et al. | Nov 2015 | B2 |
9226004 | Plante | Dec 2015 | B1 |
9235750 | Sutton et al. | Jan 2016 | B1 |
9238467 | Hoye et al. | Jan 2016 | B1 |
9240079 | Lambert et al. | Jan 2016 | B2 |
9240080 | Lambert et al. | Jan 2016 | B2 |
9245391 | Cook et al. | Jan 2016 | B2 |
9247040 | Sutton | Jan 2016 | B1 |
9251388 | Flood | Feb 2016 | B2 |
9268741 | Lambert et al. | Feb 2016 | B1 |
9275090 | Denson | Mar 2016 | B2 |
9280857 | Lambert et al. | Mar 2016 | B2 |
9292980 | Cook et al. | Mar 2016 | B2 |
9298575 | Tamari et al. | Mar 2016 | B2 |
9317980 | Cook et al. | Apr 2016 | B2 |
9330287 | Graczyk et al. | May 2016 | B2 |
9341487 | Bonhomme | May 2016 | B2 |
9342884 | Mask | May 2016 | B2 |
9344683 | Nemat-Nasser et al. | May 2016 | B1 |
9347818 | Curotto | May 2016 | B2 |
9358926 | Lambert et al. | Jun 2016 | B2 |
9373257 | Bonhomme | Jun 2016 | B2 |
9389147 | Lambert et al. | Jul 2016 | B1 |
9390568 | Nemat-Nasser et al. | Jul 2016 | B2 |
9396453 | Hynes et al. | Jul 2016 | B2 |
9401985 | Sutton | Jul 2016 | B2 |
9403278 | Van Kampen et al. | Aug 2016 | B1 |
9405992 | Badholm et al. | Aug 2016 | B2 |
9418488 | Lambert | Aug 2016 | B1 |
9428195 | Surpi | Aug 2016 | B1 |
9442194 | Kurihara et al. | Sep 2016 | B2 |
9463110 | Nishtala et al. | Oct 2016 | B2 |
9466212 | Stumphauzer, II et al. | Oct 2016 | B1 |
9472083 | Nemat-Nasser | Oct 2016 | B2 |
9495811 | Herron | Nov 2016 | B2 |
9501690 | Nemat-Nasser et al. | Nov 2016 | B2 |
9520046 | Call et al. | Dec 2016 | B2 |
9525967 | Mamlyuk | Dec 2016 | B2 |
9546040 | Flood et al. | Jan 2017 | B2 |
9573601 | Hoye et al. | Feb 2017 | B2 |
9574892 | Rodoni | Feb 2017 | B2 |
9586756 | O'Riordan et al. | Mar 2017 | B2 |
9589393 | Botnen | Mar 2017 | B2 |
9594725 | Cook et al. | Mar 2017 | B1 |
9595191 | Surpi | Mar 2017 | B1 |
9597997 | Mitsuta et al. | Mar 2017 | B2 |
9604648 | Tamari et al. | Mar 2017 | B2 |
9633318 | Plante | Apr 2017 | B2 |
9633576 | Reed | Apr 2017 | B2 |
9639535 | Ripley | May 2017 | B1 |
9646651 | Richardson | May 2017 | B1 |
9650051 | Hoye et al. | May 2017 | B2 |
9679210 | Sutton et al. | Jun 2017 | B2 |
9685098 | Kypri | Jun 2017 | B1 |
9688282 | Cook | Jun 2017 | B2 |
9702113 | Kotaki et al. | Jul 2017 | B2 |
9707595 | Ripley | Jul 2017 | B2 |
9721342 | Mask | Aug 2017 | B2 |
9734717 | Surpi et al. | Aug 2017 | B1 |
9754382 | Rodoni | Sep 2017 | B1 |
9766086 | Rodoni | Sep 2017 | B1 |
9778058 | Rodoni | Oct 2017 | B2 |
9803994 | Rodoni | Oct 2017 | B1 |
9824336 | Borges et al. | Nov 2017 | B2 |
9824337 | Rodoni | Nov 2017 | B1 |
9829892 | Rodoni | Nov 2017 | B1 |
9834375 | Jenkins et al. | Dec 2017 | B2 |
9852405 | Rodoni et al. | Dec 2017 | B1 |
10029685 | Hubbard et al. | Jul 2018 | B1 |
10152737 | Lyman | Dec 2018 | B2 |
10198718 | Rodoni | Feb 2019 | B2 |
10204324 | Rodoni | Feb 2019 | B2 |
10210623 | Rodoni | Feb 2019 | B2 |
10255577 | Steves et al. | Apr 2019 | B1 |
10311501 | Rodoni | Jun 2019 | B1 |
10332197 | Kekalainen et al. | Jun 2019 | B2 |
10354232 | Tomlin, Jr. et al. | Jul 2019 | B2 |
10382915 | Rodoni | Aug 2019 | B2 |
10410183 | Bostick et al. | Sep 2019 | B2 |
10594991 | Skolnick | Mar 2020 | B1 |
10625934 | Mallady | Apr 2020 | B2 |
10628805 | Rodatos | Apr 2020 | B2 |
10750134 | Skolnick | Aug 2020 | B1 |
10855958 | Skolnick | Dec 2020 | B1 |
10911726 | Skolnick | Feb 2021 | B1 |
11074557 | Flood | Jul 2021 | B2 |
11128841 | Skolnick | Sep 2021 | B1 |
11140367 | Skolnick | Oct 2021 | B1 |
11172171 | Skolnick | Nov 2021 | B1 |
11222491 | Romano et al. | Jan 2022 | B2 |
11373536 | Savchenko | Jun 2022 | B1 |
11386362 | Kim | Jul 2022 | B1 |
11425340 | Skolnick | Aug 2022 | B1 |
11475416 | Patel et al. | Oct 2022 | B1 |
11475417 | Patel et al. | Oct 2022 | B1 |
11488118 | Patel | Nov 2022 | B1 |
11616933 | Skolnick | Mar 2023 | B1 |
11673740 | Leon | Jun 2023 | B2 |
11715150 | Rodoni | Aug 2023 | B2 |
11727337 | Savchenko | Aug 2023 | B1 |
11790290 | Kim et al. | Oct 2023 | B1 |
11928693 | Savchenko et al. | Mar 2024 | B1 |
20020069097 | Conrath | Jun 2002 | A1 |
20020077875 | Nadir | Jun 2002 | A1 |
20020125315 | Ogawa | Sep 2002 | A1 |
20020194144 | Berry | Dec 2002 | A1 |
20030014334 | Tsukamoto | Jan 2003 | A1 |
20030031543 | Elbrink | Feb 2003 | A1 |
20030069745 | Zenko | Apr 2003 | A1 |
20030191658 | Rajewski | Oct 2003 | A1 |
20030233261 | Kawahara et al. | Dec 2003 | A1 |
20040039595 | Berry | Feb 2004 | A1 |
20040167799 | Berry | Aug 2004 | A1 |
20050038572 | Krupowicz | Feb 2005 | A1 |
20050080520 | Kline et al. | Apr 2005 | A1 |
20050182643 | Shirvanian | Aug 2005 | A1 |
20050209825 | Ogawa | Sep 2005 | A1 |
20050234911 | Hess et al. | Oct 2005 | A1 |
20050261917 | Forget Shield | Nov 2005 | A1 |
20060235808 | Berry | Oct 2006 | A1 |
20070150138 | Plante | Jun 2007 | A1 |
20070260466 | Casella et al. | Nov 2007 | A1 |
20070278140 | Mallett et al. | Dec 2007 | A1 |
20080010197 | Scherer | Jan 2008 | A1 |
20080065324 | Muramatsu et al. | Mar 2008 | A1 |
20080077541 | Scherer et al. | Mar 2008 | A1 |
20080202357 | Flood | Aug 2008 | A1 |
20080234889 | Sorensen | Sep 2008 | A1 |
20090014363 | Gonen et al. | Jan 2009 | A1 |
20090024479 | Gonen et al. | Jan 2009 | A1 |
20090055239 | Waitkus, Jr. | Feb 2009 | A1 |
20090083090 | Rolfes et al. | Mar 2009 | A1 |
20090126473 | Porat et al. | May 2009 | A1 |
20090138358 | Gonen et al. | May 2009 | A1 |
20090157255 | Plante | Jun 2009 | A1 |
20090161907 | Healey et al. | Jun 2009 | A1 |
20100017276 | Wolff et al. | Jan 2010 | A1 |
20100071572 | Carroll et al. | Mar 2010 | A1 |
20100119341 | Flood et al. | May 2010 | A1 |
20100175556 | Kummer et al. | Jul 2010 | A1 |
20100185506 | Wolff et al. | Jul 2010 | A1 |
20100217715 | Lipcon | Aug 2010 | A1 |
20100312601 | Lin | Dec 2010 | A1 |
20110108620 | Wadden et al. | May 2011 | A1 |
20110137776 | Goad et al. | Jun 2011 | A1 |
20110208429 | Zheng et al. | Aug 2011 | A1 |
20110225098 | Wolff et al. | Sep 2011 | A1 |
20110260878 | Rigling | Oct 2011 | A1 |
20110279245 | Hynes et al. | Nov 2011 | A1 |
20110316689 | Reyes et al. | Dec 2011 | A1 |
20120029980 | Paz et al. | Feb 2012 | A1 |
20120029985 | Wilson et al. | Feb 2012 | A1 |
20120047080 | Rodatos | Feb 2012 | A1 |
20120262568 | Ruthenberg | Oct 2012 | A1 |
20120265589 | Whittier | Oct 2012 | A1 |
20120310691 | Carlsson et al. | Dec 2012 | A1 |
20130024335 | Lok | Jan 2013 | A1 |
20130039728 | Price et al. | Feb 2013 | A1 |
20130041832 | Rodatos | Feb 2013 | A1 |
20130075468 | Wadden et al. | Mar 2013 | A1 |
20130332238 | Lyle | Dec 2013 | A1 |
20130332247 | Gu | Dec 2013 | A1 |
20140060939 | Eppert | Mar 2014 | A1 |
20140112673 | Sayama | Apr 2014 | A1 |
20140114868 | Wan et al. | Apr 2014 | A1 |
20140172174 | Poss et al. | Jun 2014 | A1 |
20140214697 | Mcsweeney | Jul 2014 | A1 |
20140236446 | Spence | Aug 2014 | A1 |
20140278630 | Gates et al. | Sep 2014 | A1 |
20140379588 | Gates et al. | Dec 2014 | A1 |
20150095103 | Rajamani et al. | Apr 2015 | A1 |
20150100428 | Parkinson, Jr. | Apr 2015 | A1 |
20150144012 | Frybarger | May 2015 | A1 |
20150278759 | Harris et al. | Oct 2015 | A1 |
20150294431 | Fiorucci et al. | Oct 2015 | A1 |
20150298903 | Luxford | Oct 2015 | A1 |
20150302364 | Calzada et al. | Oct 2015 | A1 |
20150307273 | Lyman | Oct 2015 | A1 |
20150324760 | Borowski | Nov 2015 | A1 |
20150326829 | Kurihara et al. | Nov 2015 | A1 |
20150348252 | Mask | Dec 2015 | A1 |
20150350610 | Loh | Dec 2015 | A1 |
20160021287 | Loh | Jan 2016 | A1 |
20160044285 | Gasca et al. | Feb 2016 | A1 |
20160179065 | Shahabdeen | Jun 2016 | A1 |
20160187188 | Curotto | Jun 2016 | A1 |
20160224846 | Cardno | Aug 2016 | A1 |
20160232498 | Tomlin, Jr. et al. | Aug 2016 | A1 |
20160239689 | Flood | Aug 2016 | A1 |
20160247058 | Kreiner et al. | Aug 2016 | A1 |
20160292653 | Gonen | Oct 2016 | A1 |
20160300297 | Kekalainen et al. | Oct 2016 | A1 |
20160321619 | Inan | Nov 2016 | A1 |
20160334236 | Mason et al. | Nov 2016 | A1 |
20160335814 | Tamari et al. | Nov 2016 | A1 |
20160372225 | Lefkowitz et al. | Dec 2016 | A1 |
20160377445 | Rodoni | Dec 2016 | A1 |
20160379152 | Rodoni | Dec 2016 | A1 |
20160379154 | Rodoni | Dec 2016 | A1 |
20170008671 | Whitman et al. | Jan 2017 | A1 |
20170011363 | Whitman et al. | Jan 2017 | A1 |
20170029209 | Smith et al. | Feb 2017 | A1 |
20170046528 | Lambert | Feb 2017 | A1 |
20170061222 | Hoye et al. | Mar 2017 | A1 |
20170076249 | Byron et al. | Mar 2017 | A1 |
20170081120 | Liu et al. | Mar 2017 | A1 |
20170086230 | Azevedo et al. | Mar 2017 | A1 |
20170109704 | Lettieri et al. | Apr 2017 | A1 |
20170116583 | Rodoni | Apr 2017 | A1 |
20170116668 | Rodoni | Apr 2017 | A1 |
20170118609 | Rodoni | Apr 2017 | A1 |
20170121107 | Flood et al. | May 2017 | A1 |
20170124533 | Rodoni | May 2017 | A1 |
20170154287 | Kalinowski et al. | Jun 2017 | A1 |
20170176986 | High et al. | Jun 2017 | A1 |
20170193798 | Call et al. | Jul 2017 | A1 |
20170200333 | Plante | Jul 2017 | A1 |
20170203706 | Reed | Jul 2017 | A1 |
20170221017 | Gonen | Aug 2017 | A1 |
20170243269 | Rodini et al. | Aug 2017 | A1 |
20170243363 | Rodini | Aug 2017 | A1 |
20170277726 | Huang et al. | Sep 2017 | A1 |
20170308871 | Tallis | Oct 2017 | A1 |
20170330134 | Botea et al. | Nov 2017 | A1 |
20170344959 | Bostick et al. | Nov 2017 | A1 |
20170345169 | Rodoni | Nov 2017 | A1 |
20170350716 | Rodoni | Dec 2017 | A1 |
20170355522 | Salinas et al. | Dec 2017 | A1 |
20170364872 | Rodoni | Dec 2017 | A1 |
20180012172 | Rodoni | Jan 2018 | A1 |
20180025329 | Podgorny et al. | Jan 2018 | A1 |
20180075417 | Gordon et al. | Mar 2018 | A1 |
20180158033 | Woods et al. | Jun 2018 | A1 |
20180194305 | Reed | Jul 2018 | A1 |
20180224287 | Rodini et al. | Aug 2018 | A1 |
20180245940 | Dong et al. | Aug 2018 | A1 |
20180247351 | Rodoni | Aug 2018 | A1 |
20190005466 | Rodoni | Jan 2019 | A1 |
20190019167 | Candel et al. | Jan 2019 | A1 |
20190050879 | Zhang et al. | Feb 2019 | A1 |
20190056416 | Rodoni | Feb 2019 | A1 |
20190065901 | Amato et al. | Feb 2019 | A1 |
20190121368 | Bussetti et al. | Apr 2019 | A1 |
20190196965 | Zhang et al. | Jun 2019 | A1 |
20190197498 | Gates et al. | Jun 2019 | A1 |
20190210798 | Schultz | Jul 2019 | A1 |
20190217342 | Parr et al. | Jul 2019 | A1 |
20190244267 | Rattner et al. | Aug 2019 | A1 |
20190311333 | Kekalainen et al. | Oct 2019 | A1 |
20190360822 | Rodoni et al. | Nov 2019 | A1 |
20190385384 | Romano et al. | Dec 2019 | A1 |
20200082167 | Shalom et al. | Mar 2020 | A1 |
20200082354 | Kurani | Mar 2020 | A1 |
20200109963 | Zass | Apr 2020 | A1 |
20200175556 | Podgorny | Jun 2020 | A1 |
20200189844 | Sridhar | Jun 2020 | A1 |
20200191580 | Christensen et al. | Jun 2020 | A1 |
20200401995 | Aggarwala | Dec 2020 | A1 |
20210024068 | Lacaze | Jan 2021 | A1 |
20210060786 | Ha | Mar 2021 | A1 |
20210188541 | Kurani et al. | Jun 2021 | A1 |
20210217156 | Balachandran et al. | Jul 2021 | A1 |
20210345062 | Koga | Nov 2021 | A1 |
20210371196 | Krishnamurthy | Dec 2021 | A1 |
20220118854 | Davis | Apr 2022 | A1 |
20230117427 | Turner | Apr 2023 | A1 |
Number | Date | Country |
---|---|---|
2632738 | May 2016 | CA |
2632689 | Oct 2016 | CA |
101482742 | Jul 2009 | CN |
101512720 | Aug 2009 | CN |
105787850 | Jul 2016 | CN |
105929778 | Sep 2016 | CN |
106296416 | Jan 2017 | CN |
209870019 | Dec 2019 | CN |
69305435 | Apr 1997 | DE |
69902531 | Apr 2003 | DE |
102012006536 | Oct 2013 | DE |
577540 | Oct 1996 | EP |
1084069 | Aug 2002 | EP |
2028138 | Feb 2009 | EP |
2447184 | Sep 2008 | GB |
2508209 | May 2014 | GB |
3662616 | Jun 2005 | JP |
2012-206817 | Oct 2012 | JP |
2013-142037 | Jul 2013 | JP |
9954237 | Oct 1999 | WO |
2007067772 | Jun 2007 | WO |
2007067775 | Jun 2007 | WO |
2012069839 | May 2012 | WO |
2012172395 | Dec 2012 | WO |
2016074608 | May 2016 | WO |
2016187677 | Dec 2016 | WO |
2017070228 | Apr 2017 | WO |
2017179038 | Oct 2017 | WO |
2018182858 | Oct 2018 | WO |
2018206766 | Nov 2018 | WO |
2018215682 | Nov 2018 | WO |
2019051340 | Mar 2019 | WO |
Entry |
---|
US 9,092,921 B2, 07/2015, Lambert et al. (withdrawn) |
Nilopherjan, N. et al.; Automatic Garbage Volume Estimation Using SIFT Features Through Deep Neural Networks and Poisson Surface Reconstruction; International Journal of Pure and Applied Mathematics; vol. 119, No. 14; 2015; pp. 1101-1107. |
Ghongane, Aishwarya et al; Automatic Garbage Tracking and Collection System; International Journal of Advanced Technology in Engineering and Science; vol. 5, No. 4; Apr. 2017; pp. 166-173. |
Rajani et al.; Waste Management System Based on Location Intelligence; 4 pages; Poojya Doddappa Appa Colleage of Engineering, Kalaburgi. |
Waste Management Review; A clear vison on waste collections; Dec. 8, 2015; 5 pages; http://wastemanagementreiew.com/au/a-clear-vison-on-waste-collections/. |
Waste Management Surveillance Solutiosn; Vehicle Video Cameral; Aug. 23, 2017; 6 pages; http://vehiclevideocameras.com/mobile-video-applications/waste-management-camera.html. |
Rich, John I.; Truck Equipment: Creating a Safer Waste Truck Environment; Sep. 2013; pp. 18-20; WasteAdvantage Magainze. |
Town of Prosper; News Release: Solid Waste Collection Trucks Equipped wit “Third Eye,” video system aborad trash and recycling trucks to improve service; Jan. 13, 2017; 1 page; U.S. |
Product News Network; Telematics/Live Video System Increases Driver Safety/Productivity; Mar. 30, 2015; 3 pages; Thomas Industrial Network, Inc. |
Karidis, Arlene; Waste Pro to Install Hight-Tech Camera Systems in all Trucks to Address Driver Safety; Mar. 10, 2016; 2 pages; Wastedive.com. |
Greenwalt, Megan; Finnish Company Uses IoT to Digitize Trash Bins; Sep. 14, 2016; 21 pages; www.waste360.com. |
Georgakopoulos, Chris; Cameras Cut Recycling Contamination; The Daily Telegraph; Apr. 7, 2014; 2 pages. |
Van Dongen, Matthew; Garbage ‘Gotcha’ Videos on Rise in City: Residents Irked Over Perceived Infractions; Nov. 18, 2015; 3 pages; The Spectator. |
The Advertiser; Waste Service Drives Innovation; Jan. 25, 2016; 2 pages; Fairfax Media Publications Pty Limited; Australia. |
rwp-wasteportal.com; Waste & Recycling Data Portal and Software; 16 pages; printed Oct. 3, 2019. |
Bhargava, Hermant K. et al.; A Web-Based Decision Support System for Waste Disposal and Recycling; pp. 47-65; 1997; Comput.Environ. and Urban Systems, vol. 21, No. 1; Pergamon. |
Kontokasta, Constantine E. et al.; Using Machine Learning and Small Area Estimation to Predict Building-Level Municipal Solid Waste Generation in Cities; pp. 151-162; 2018; Computer, Envieonment and Urban Systems; Elsevier. |
Ferrer, Javier et al.; BIN-CT: Urban Waste Collection Based on Predicting the Container Fill Level; Apr. 23, 2019; 11 pages; Elsevier. |
Vu, Hoang Lan et al.; Waste Management: Assessment of Waste Characteristics and Their Impact on GIS Vechicle Collection Route Optimization Using ANN Waste Forecasts; Environmental Systems Engineering; Mar. 22, 2019; 13 pages; Elsevier. |
Hina, Syeda Mahlaqa; Municipal Solid Waste Collection Route Optimization Using Geospatial Techniques: A Case Study of Two Metropolitan Cities of Pakistan; Feb. 2016; 205 pages; U.S. |
Kannangara, Miyuru et al.; Waste Management: Modeling and Prediction of Regional Municipal Soid Waste Generation and Diversion in Canada Using Machine Learning Approaches; Nov. 30, 2017; 3 pages; Elsevier. |
Tan, Kah Chun et al.; Smart Land: AI Waste Sorting System; University of Malaya; 2 pages; Keysight Techonogies. |
Oliveira, Veronica et al.; Journal of Cleaner Production: Artificial Neural Network Modelling of the Amount of Separately-Collected Household Packaging Waste; Nov. 8, 2018; 9 pages; Elsevier. |
Zade, Jalili Ghazi et al.; Prediction of Municipal Solid Waste Generation by Use of Artificial Neural Network: A Case Study of Mashhad; Winter 2008; 10 pages; Int. J. Environ. Res., 2(1). |
Sein, Myint Myint et al.; Trip Planning Query Based on Partial Sequenced Route Algorithm; 2019 IEEE 8th Global Conference; pp. 778-779. |
A.F., Thompson et al.; Application of Geographic Information System to Solid Waste Management; Pan African International Conference on Information Science, Computing and Telecommunications; 2013; pp. 206-211. |
Malakahmad, Amirhossein et al.; Solid Waste Collection System in Ipoh City, A Review; 2011 International Conference on Business, Engineering and Industrial Applications; pp. 174-179. |
Ali, Tariq et al.; IoT-Based Smart Waste Bin Monitoring and Municipal Solid Waste Manaement System for Smart Cities; Arabian Journal for Science and Engineering; Jun. 4, 2020; 14 pages. |
Alfeo, Antonio Luca et al.; Urban Swarms: A new approch for autonomous waste management; Mar. 1, 2019; 8 pages. |
Jwad, Zainab Adnan et al.; An Optimization Approach for Waste Collection Routes Based on GIS in Hillah-Iraq; 2018; 4 pages; Publisher: IEEE. |
Chaudhari, Sangita S. et al.; Solid Waste Collection as a Service using IoT-Solution for Smart Cities; 2018; 5 pages; Publisher: IEEE. |
Burnley, S.J. et al.; Assessing the composition of municipal solid waste in Wales; May 2, 2006; pp. 264-283; Elsevier B.V. |
Lokuliyana, Shashika et al.; Location based garbage management system with loT for smart city; 13th ICCSE; Aug. 8-11, 2018; pp. 699-703. |
Number | Date | Country | |
---|---|---|---|
63326708 | Apr 2022 | US |